In the last two decades, the high-dimensional data production, such as metabolomics, proteomics, transcriptomics, and genomics, increased considerably ( Zhu et al., 2008 McKenzie et al., 2016). Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods. Results based on simulated scenarios suggest that the statistical power of BioNetStat is less sensitive to the increase of the number of networks than Gene Set Coexpression Analysis ( GSCA). The latter related to gene expression of tumor cells and plant metabolism. We evaluated the performance of BioNetStat in both, toy models and two case studies. For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states. The analysis of the structural alterations on the network reveals significant modifications in the system. BioNetStat compares correlation networks based on the probability distribution of a feature of the graph (e.g., centrality measures). To compare two or more networks simultaneously, we developed BioNetStat, a Bioconductor package with a user-friendly graphical interface. However, biological systems often present more than two biological states (e.g., tumor grades). Most of these methods focus on the comparison of two biological networks (e.g., control vs. The study of interactions among biological components can be carried out by using methods grounded on network theory. 2Department of Botany, Institute of Biosciences, University of São Paulo, São Paulo, Brazil.1Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.Vinícius Carvalho Jardim 1,2, Suzana de Siqueira Santos 1, Andre Fujita 1 and Marcos Silveira Buckeridge 2 *
0 kommentar(er)
